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Sample: 100 most traded ETF in USA.
Data: Weekly quotes.
Dates: 21/10/2021 - 25/04/2022.
Quantity: 19 weekly quotes per security (1.800 andlesticks in total).
Method: Markov General Model + Pattern Search to Decimals
Breakthroughs:
Not a regression problem anymore but categorical.
Not focus on quotes but on quote changes (delta price).
Approach changed, instead of identifying patterns through time-series' surveilance (technical analysis) time-series freely oscilate over price change patterns.
No fundamental analysis (all information is reflected on the price change assumption).
Not multiple variables (inflation, benchmarks, etc.) but one (delta price).
Outcomes:
Accuracy at 80,30% while precision at 86,97%. Confusion matrix (values in %):
| PN² | PP | |
AN | 1,18 | 11,85 | 13,03 |
AP | 7,85 | 79,12 | 86,97 |
| 9,03 | 90,97 | |
2. Works over sectors and markets (14 examples out of 100):
iShares Global Clean Energy
iShares 7-10 Years Treasury Bond
VanEck Junior Gold Miners
iShares MSCI Brazil
iShares Russell 2000
SPDR Bloomberg High Yield Bond
SPDR S&P Regional Banking
KraneShares CSI China Internet
SPDR S&P 500
United States Oil Fund
Vanguard Emerging Markets Stock Index Fund
Consumer Staples Select Sector SPDR Fund
Health Care Select Sector SPDR Fund
Financial Select Sector SPDR Fund
¹Average of the sample yields to this valıue, individual values oscilate between 71% and 97%.
²PN: Predicted negative means the probability, the model estimated, of the actual price not ending up within the predicted range.
All these calculations are based on probabilities, which can fail sometimes; however, the developed algorithm to reach those numbers has been thought to reduce such failures to their lowest level.